CAREER: Declarative Uncertainty

职业：声明的不确定性

• 批准号: 1750460
• 负责人: Oliver Kennedy
• 金额: $ 54.23万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750460&HistoricalAwards=false
• 关键词: CAREER; Declarative; Uncertainty

Data is messy. Fortunately, with minimal human intervention, good data cleaning heuristics produce mostly reliable, usually actionable information from big, messy data. For instance, analysts might automate their curation workflows by using classifiers to predict missing attribute values, or by using an entity-resolver to find and merge duplicate records. Unfortunately, heuristics are also dangerous, as the result of heuristic curation is often taken as fact. Serious mistakes like people being denied a loan due to someone else's bad credit, 12-year olds being identified as terrorists, or billion dollar investment errors, often result when low-confidence, or uncertain heuristic inferences are treated as truth. Many principled tools like probabilistic databases already exist for automatically tracking potential errors in unreliable data, but these tools are not easy to use. As a result, analysts more often resort to simply documenting potential errors and hoping that anyone using the data will realize the implications. This proposal will enable data management systems that can query and organize uncertain data, without being hard to use. The specific aim of this proposal is to decouple the process of asking questions about uncertain data from mechanical concerns like why the data is uncertain, how the user wants to view uncertainty in query results, or which algorithms should be used. To enable this sort of "declarative uncertainty management," the project team will build on a system called Mimir that virtualizes uncertainty by augmenting data curation workflows (e.g., ETL pipelines) with a form of provenance capture through which heuristics can register alternative outputs (e.g., a schema matcher may register multiple potential matches). This provenance can then be used to synthesize a wide range of different physical and visual representations of uncertainty in data and in query results. To enable declarative uncertainty management, this proposal will address specific problems that fall into two general categories: (1) selecting and efficiently constructing qualitative summaries of uncertainty in query results, and (2) enhancing database query compilers and optimizers to support practical, efficient query processing over uncertain data. For further information see the project web page: http://mimirdb.infoThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据很混乱。 幸运的是，通过最少的人干预，良好的数据清洁启发式方法大多可从大而混乱的数据中产生可靠的，通常可操作的信息。 例如，分析师可以使用分类器预测缺失属性值或使用实体 - 星期来查找和合并重复记录来自动化其策划工作流程。 不幸的是，由于启发式策划的结果，启发式方法也很危险。 严重的错误，例如由于别人的不良信誉，被确定为恐怖分子或十亿美元的投资错误而被拒绝贷款的严重错误，通常在低信任或不确定的启发式推论被视为真理时通常会导致。 许多原则性的工具（例如概率数据库）已经存在自动跟踪不可靠数据中的潜在错误，但是这些工具不容易使用。 结果，分析师通常会求助于简单地记录潜在错误，并希望任何使用数据的人都会意识到含义。 该建议将启用可以查询和组织不确定数据的数据管理系统，而无需使用。该提案的具体目的是使从机械关注的不确定数据提出问题的过程中，例如为什么数据不确定，用户希望如何查看查询结果中的不确定性或应该使用哪些算法。 为了启用这种“声明性不确定性管理”，项目团队将建立在一个名为Mimir的系统上，该系统通过增强数据策展工作流（例如ETL管道）通过一种出处捕获形式来通过启发式捕获可以注册替代输出（例如，A架构匹配器可以注册多个潜在的竞争）来虚拟地使不确定性。 然后，该出处可用于综合数据和查询结果中不确定性的各种不同的物理和视觉表示。 为了启用声明性的不确定性管理，该提案将解决属于两个一般类别的特定问题：（1）在查询结果中选择并有效地构建不确定性的定性摘要，以及（2）增强数据库查询编译器和优化者，以支持实用，有效的查询处理，而不是不确定的数据。有关更多信息，请参见项目网页：http：//mimirdb.infothis Award反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，认为值得通过评估来获得支持。

项目成果

Runtime provenance refinement for notebooks

笔记本的运行时出处细化

• DOI: 10.1145/3530800.3534535
• 发表时间: 2022
• 期刊: Proceedings of the 14th International Workshop on the Theory and Practice of Provenance
• 作者: Deo, Nachiket;Glavic, Boris;Kennedy, Oliver
• 通讯作者: Kennedy, Oliver

Loki: Streamlining Integration and Enrichment

Loki：简化集成和丰富

• 发表时间: 2020
• 期刊: Human in the Loop Data Analytics
• 作者: Spoth, William;Kumari, Poonam;Kennedy, Oliver;Nargesian, Fatemeh
• 通讯作者: Nargesian, Fatemeh

Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers

不确定性注释数据库 - 近似某些答案的轻量级方法

• DOI: 10.1145/3299869.3319887
• 发表时间: 2019
• 期刊: SIGMOD
• 作者: Feng, Su;Huber, Aaron;Glavic, Boris;Kennedy, Oliver
• 通讯作者: Kennedy, Oliver

DataSense: Display Agnostic Data Documentation

DataSense：显示不可知的数据文档

• 发表时间: 2021
• 期刊: Conference on Innovative Data Systems Research
• 作者: Kumari, Poonam;Brachmann, Michael;Kennedy, Oliver;Feng, Su;Glavic, Boris
• 通讯作者: Glavic, Boris

Query Log Compression for Workload Analytics

用于工作负载分析的查询日志压缩

• DOI: 10.14778/3291264.3291265
• 发表时间: 2018
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Xie, Ting;Chandola, Varun;Kennedy, Oliver
• 通讯作者: Kennedy, Oliver